The current construct of schizophrenia as a unitary disease is far from satisfactory, and is in need of reconceptualization. The first five papers in our “facts” series reviewed what is known about schizophrenia to date, and a limited number of key facts appear to stand out. Schizophrenia is characterized by persistent cognitive deficits, positive and negative symptoms typically beginning in youth, substantive heritability, and brain structural, functional and neurochemical alterations including dopaminergic dysregulation. Several pathophysiological models have been proposed with differing interpretations of the illness, like the fabled six blind Indian men groping different parts of an elephant coming up with different conclusions. However, accumulating knowledge is integrating the several extant models of schizophrenia etiopathogenesis into unifying constructs; we discuss an example, involving a neurodevelopmental imbalance in excitatory/inhibitory neural systems leading to impaired neural plasticity. This imbalance, which may be proximal to clinical manifestations, could result from a variety of genetic, epigenetic and environmental causes, as well as pathophysiological processes such as inflammation and oxidative stress. Such efforts to “connect the dots” (and visualizing the elephant) are still limited by the substantial clinical, pathological, and etiological heterogeneity of schizophrenia and its blurred boundaries with several other psychiatric disorders leading to a “fuzzy cluster” of overlapping syndromes, thereby reducing the content, discriminant and predictive validity of a unitary construct of this illness. The way ahead involves several key directions: a) choosing valid phenotype definitions increasingly derived from translational neuroscience; b) addressing clinical heterogeneity by a cross-diagnostic dimensional and a staging approach to psychopathology; c) addressing pathophysiological heterogeneity by elucidating independent families of “extended” intermediate phenotypes and pathophysiological processes (e.g. altered excitatory/inhibitory, salience or executive circuitries, oxidative stress systems) that traverse structural, functional, neurochemical and molecular domains; d) resolving etiologic heterogeneity by mapping genomic and environmental factors and their interactions to syndromal and specific pathophysiological signatures; e) separating causal factors from consequences and compensatory phenomena; and f) formulating or reformulating hypotheses that can be refuted/tested, perhaps in the mouse or other experimental models. These steps will likely lead to the current entity of schizophrenia being usefully deconstructed and reconfigured into phenotypically overlapping, but etiopathologically unique and empirically testable component entities (similar to mental retardation, epilepsy or cancer syndromes). The mouse may be the way to rescue the trapped elephant!

Although offering many benefits for several psychiatric disorders, antipsychotic drugs (APDs) as a class have a major liability in their tendency to promote adiposity, obesity, and metabolic dysregulation in an already metabolically vulnerable population. The past decade has witnessed substantial research aimed at investigating the mechanisms of these adverse effects and mitigating them. On July 11 and 12, 2011, with support from 2 NIH institutes, leading experts convened to discuss current research findings and to consider future research strategies. Five areas where significant advances are being made emerged from the conference: (1) methodological issues in the study of APD effects; (2) unique characteristics and needs of pediatric patients; (3) genetic components underlying susceptibility to APD-induced metabolic effects; (4) APD effects on weight gain and adiposity in relation to their acute effects on glucose regulation and diabetes risk; and (5) the utility of behavioral, dietary, and pharmacological interventions in mitigating APD-induced metabolic side effects. This paper summarizes the major conclusions and important supporting data from the meeting.

Paliperidone palmitate is a long-acting injectable antipsychotic agent. This 13-week, multicenter, randomized (1 : 1 : 1 : 1), double-blind, parallel-group study evaluated the efficacy, safety, and tolerability of fixed 25, 50, and 100 milligram equivalent (mg equiv.) doses of paliperidone palmitate vs placebo administered as gluteal injections on days 1 and 8, then every 4 weeks (days 36 and 64) in 518 adult patients with schizophrenia. The intent-to-treat analysis set (N=514) was 67% men and 67% White, with a mean age of 41 years. All paliperidone palmitate dose groups showed significant improvement vs placebo in the Positive and Negative Syndrome Scale (PANSS) total score (primary efficacy measure; 25 and 50 mg equiv., p=0.02; 100 mg equiv., p<0.001), as well as Clinical Global Impression Severity scores (p⩽0.006) and PANSS negative and positive symptom Marder factor scores (p⩽0.04). The Personal and Social Performance scale showed no significant difference between treatment groups. The overall incidence of treatment-emergent adverse events was similar between groups. Parkinsonism, the most frequently reported extrapyramidal symptom, was reported at similar rates for placebo (5%) and paliperidone palmitate (5–6% across doses). The mean body mass index and mean weight showed relatively small dose-related increases during paliperidone palmitate treatment. Investigator-evaluated injection-site pain, swelling, redness, and induration were similar across treatment groups; scores for patient-evaluated injection-site pain (visual analog scale) were similar across groups and diminished with time. All doses of once-monthly paliperidone palmitate were efficacious and generally tolerated, both locally and systemically. Paliperidone palmitate offers the potential to improve outcomes in adults with symptomatic schizophrenia.

Suicide is the second leading cause of death among 25–34 year olds and the third leading cause of death among 15–25 year olds in the United States. In the Emergency Department, where suicidal patients often present, estimating the risk of repeated attempts is generally left to clinical judgment. This paper presents our second attempt to determine the role of computational algorithms in understanding a suicidal patient’s thoughts, as represented by suicide notes. We focus on developing methods of natural language processing that distinguish between genuine and elicited suicide notes. We hypothesize that machine learning algorithms can categorize suicide notes as well as mental health professionals and psychiatric physician trainees do. The data used are comprised of suicide notes from 33 suicide completers and matched to 33 elicited notes from healthy control group members. Eleven mental health professionals and 31 psychiatric trainees were asked to decide if a note was genuine or elicited. Their decisions were compared to nine different machine-learning algorithms. The results indicate that trainees accurately classified notes 49% of the time, mental health professionals accurately classified notes 63% of the time, and the best machine learning algorithm accurately classified the notes 78% of the time. This is an important step in developing an evidence-based predictor of repeated suicide attempts because it shows that natural language processing can aid in distinguishing between classes of suicidal notes.

Objective

In mice and in humans, treatment with the second generation antipsychotic drug olanzapine (OLZ) produces excessive weight gain, adiposity and secondary metabolic complications, including loss of glucose and insulin homeostasis. In mice consuming a high fat (HF) diet, a similar phenotype develops, which is inhibited by the analgesic acetaminophen (APAP) and by the antioxidant tetrahydroindenoindole (THII). Therefore, we examined the ability of APAP and THII to prevent metabolic changes in mice receiving OLZ.

Design and Measurement

C57BL/6J mice received either a normal diet or a high fat diet, and were administered OLZ (3 mg/kg body weight/d), alone or with APAP (35 mg/kg body weight/d) or THII (4.5 mg/kg body weight), for 10 weeks. Parameters of body composition and metabolism, including glucose and insulin homeostasis and oxidative stress, were examined.

Results

OLZ treatment doubled the HF diet-induced increases in body weight and percent body fat. These increases were partially prevented by both APAP and THII, although food consumption was constant in all groups. The THII protection was associated with an increase in whole body and mitochondrial respiration. OLZ also exacerbated, and both APAP and THII prevented, HF diet-induced loss of glucose tolerance and insulin resistance. Since increased body fat promotes insulin resistance by a pathway involving oxidative stress, we evaluated production of reactive oxygen and lipid peroxidation in white adipose tissue (WAT). HF diet caused an increase in lipid peroxidation, NADPH-dependent O2 uptake and H2O2 production, which were further exacerbated by OLZ. APAP, THII, and the NADPH oxidase inhibitor, diphenyleneiodonium chloride (DPI) each abolished oxidative stress in WAT.

Conclusions

We conclude that both APAP and THII intervene in the development of obesity and metabolic complications associated with OLZ treatment.

Objective

Persons with schizophrenia die earlier than the general population, in large part due to cardiovascular disease. The study objective was to examine effects of different antipsychotic treatments on estimates of 10 year coronary heart disease (CHD) risk calculated by the Framingham Heart Study formula.

Method

Change in ten-year risk for CHD was compared between treatment groups in 1125 patients followed for 18 months or until treatment discontinuation in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) Schizophrenia Trial.

Results

The covariate-adjusted mean change in 10-year CHD risk differed significantly between treatments. Olanzapine was associated with a 0.5% (SE 0.3) increase and quetiapine, a 0.3% (SE 0.3) increase; whereas risk decreased in patients treated with perphenazine, −0.5% (SE 0.3), risperidone, −0.6% (SE 0.3), and ziprasidone −0.6% (SE 0.4). The difference in 10-year CHD risk between olanzpaine and risperidone was statistically significant (p=0.004). Differences in estimated 10 year CHD risk between drugs were most marked in the tertile of subjects with a baseline CHD risk of at least 10%. Among individual CHD risk factors used in the Framingham formula, only total and HDL cholesterol levels differed between treatments.

Conclusions

These results indicate that the impact on 10-year CHD risk differs significantly between antipsychotic agents, with olanzapine producing the largest elevation in CHD risk of the agents studied in CATIE.

Background

Recent literature documents a stronger association between nonfasting triglycerides (TG) and cardiovascular risk compared to fasting TG. Given concerns over antipsychotic effects on serum TG, this analysis explored changes in nonfasting TG in phase 1 of the CATIE Schizophrenia Trial.

Methods

Change in nonfasting TG, adjusted for baseline value, was compared between antipsychotic treatment groups using subjects with nonfasting laboratory assessments at baseline and 3 months.

Results

Among the 246 subjects there were significant treatment differences in 3-month change from baseline (p=0.009). The greatest increases in median and adjusted mean nonfasting TG levels were seen among those randomized to quetiapine (mean +54.7 mg/dl, median +26 mg/dl) and olanzapine (mean +23.4 mg/dl, median +26.5 mg/dl), while ziprasidone was neutral (mean +0.0 mg/dl, median + 8 mg/dl), and decreases were seen with risperidone (mean −18.4 mg/dl, median −6.5 mg/dl) and perphenazine (mean −1.3 mg/dl, median −22 mg/dl). Pairwise comparisons indicated a significant between-group difference for perphenazine vs. olanzapine (p=0.002) and a trend for perphenazine vs. quetiapine (p=0.006).

Conclusions

This analysis provides further evidence for differential antipsychotic metabolic liabilities, and confirms signals for the effects of olanzapine and quetiapine on serum TG seen in earlier CATIE analyses. Future consensus recommendations will clarify the role of nonfasting TG monitoring in routine clinical practice.